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2023 International Conference on New Frontiers in Communication, Automation, Management and Security, ICCAMS 2023

作者： Pooja Ahuja, S. Likhith, S.R. Shiva Balan, A. Navya, K. Bengaluru India Bio-intelligence Lab Presidency University Department of School of Computer Science and Engineering and Information Science Bengaluru India Nagarjuna College of Engineering and Technology Department of Ise Bengaluru India

ISBN: (纸本)9798350317060

Unique Clinical pictures have a basic impact on the specialist's capability to the right conclusion and in the treatment of the patient. Diagnostic imaging can quickly identify lesions thanks to sophisticated algorithms, and it is essential to glean characteristics from photographs. In a number of studies, a number of algorithms have been incorporated into imaging in medicine. Using convolutional neural network, CNN's fundamental architecture is constructed using picture component extraction. To be able to circumvent the limitations imposed by both machine vision and human vision, the study is extended to include CNN with multiple input channels for visual feature extraction. This study uses approximately 3300 MRI samples from Kaggle to investigate four classifications: tumors of the pituitary gland, meningioma, glioma, and none. The implemented BrainNet has a accuracy of 98.31 percent and a validation accuracy of 87.80 percent. In order to determine which strategy performed better, deep architectures like InceptionNet and ResNet were also put through tests with or without learning through transfer. © 2023 IEEE.

关键词： Image segmentation

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Joint Optimization of Compression, Transmission and Computation for Cooperative Perception Aided Intelligent Vehicular Networks

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IEEE Transactions on Vehicular Technology 2025年第5期74卷 8201-8214页

作者： Lu, Binbin Huang, Xumin Wu, Yuan Qian, Liping Zhou, Sheng Niyato, Dusit University of Macau State Key Laboratory of Internet of Things for Smart City Department of Computer and Information Science 999078 China Guangdong University of Technology School of Automation Guangzhou510006 China University of Macau State Key Lab of Internet of Things for Smart City 999078 China University of Macau State Key Laboratory of Internet of Things for Smart City Department of Computer Information Science 999078 China Zhuhai UM Science and Technology AQ2 Research Institute Zhuhai519301 China Zhejiang University of Technology Institute of Cyberspace Security Hangzhou310023 China Tsinghua University Beijing National Research Center for Information Science and Technology Department of Electronic Engineering Beijing100084 China Nanyang Technological University School of Computer Science and Engineering 639815 Singapore

Cooperative perception is a promising paradigm to tackle the perception limitations of a single intelligent vehicle (IV) to enhance the driving safety and efficiency in intelligent vehicular networks. However, the real-time transmission and computation-intensive fusion of raw sensing data raise new challenges for satisfying the stringent delay requirement of delay-sensitive applications. In this paper, we formulate a joint optimization problem of the cooperative IV selection, compression ratio selection, transmit power control, task offloading decision and computation allocation to minimize the end-to-end delay consisting of compression, transmission and computation. In particular, to meet the perception probability requirement, the quality and quantity-aware matching algorithm is proposed to optimize the cooperative IV selection. To guarantee the queue stability of offloading tasks, we develop the Lyapunov optimization algorithm to determine the upper bound of the offloading tasks and the corresponding optimal computation allocation. The Lyapunov aided deep reinforcement learning algorithm is further proposed to dynamically adjust the task offloading decision and compression ratio selection to minimize the end-to-end delay with the transmit power control being optimized by the successive convex approximation algorithm. Simulation results demonstrate that, compared to several benchmark algorithms, our proposed algorithm achieves the lowest end-to-end delay while guaranteeing the requirements on perception probability and queue stability effectively. © 1967-2012 IEEE.

关键词： Deep reinforcement learning

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The SWH-Analytics Framework 2

The SWH-Analytics Framework

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2nd Italian Conference on Big Data and Data science, ITADATA 2023

作者： Antelmi, Alessia Torquati, Massimo Gregori, Daniele Polzella, Francesco Spinatelli, Gianmarco Aldinucci, Marco Computer Science Department University of Torino Italy Computer Science Department University of Pisa Italy HPC-KTT national lab CINI Italy E4 Computer Engineering SpA Italy Zerodivision systems Srl Italy

The Software Heritage (SWH) dataset serves as a vast repository for open-source code, with the ambitious goal of preserving all publicly available open-source projects. Despite being designed to effectively archive project files, its size of nearly 1 petabyte presents challenges in efficiently supporting Big Data MapReduce or AI systems. To address this disparity and enable seamless custom analytics on the SWH dataset, we present the SWH-Analytics (SWHA) architecture. This development environment quickly and transparently runs custom analytic applications on open-source software data preserved over time by SWH. © 2023 Copyright for this paper by its authors.

关键词： Open source software

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DPN: Decoupling Partition and Navigation for Neural Solvers of Min-max Vehicle Routing Problems 41

DPN: Decoupling Partition and Navigation for Neural Solvers ...

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41st International Conference on Machine Learning, ICML 2024

作者： Zheng, Zhi Yao, Shunyu Wang, Zhenkun Tong, Xialiang Yuan, Mingxuan Tang, Ke Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China Department of Computer Science City University of Hong Kong Hong Kong School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen China China Huawei Noah's Ark Lab Shenzhen China

The min-max vehicle routing problem (min-max VRP) traverses all given customers by assigning several routes and aims to minimize the length of the longest ***, reinforcement learning (RL)-based sequential planning methods have exhibited advantages in solving efficiency and ***, these methods fail to exploit the problem-specific properties in learning representations, resulting in less effective features for decoding optimal *** paper considers the sequential planning process of min-max VRPs as two coupled optimization tasks: customer partition for different routes and customer navigation in each route (i.e., partition and navigation).To effectively process min-max VRP instances, we present a novel attention-based Partition-and-Navigation encoder (P&N Encoder) that learns distinct embeddings for partition and ***, we utilize an inherent symmetry in decoding routes and develop an effective agent-permutation-symmetric (APS) loss *** results demonstrate that the proposed Decoupling-Partition-Navigation (DPN) method significantly surpasses existing learning-based methods in both single-depot and multi-depot minmax *** code is available at. Copyright 2024 by the author(s)

关键词： Sales

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The impact of emotional valence on students learning performance and evaluation: A text mining of students' opinion data 4

The impact of emotional valence on students learning perform...

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4th International Conference on Electrical, computer, Communications and Mechatronics engineering, ICECCME 2024

作者： Okoye, Kingsley Institute for Future of Education School of Engineering and Sciences Tecnologico de Monterrey Writing Lab Department of Computer Science Nuevo LeonCP 64849 Mexico

ISBN: (纸本)9798350391183

Emotions classification or valence extraction in textual datasets, *** students' opinion data, is becoming an emerging topic aimed at understanding the impact or intensities of words (terms) used by the users in the different contexts/applications. The method (emotional valence) for textual data scrutiny and exploration have proved to be central to the human experience analysis. To this effect, this study implements a text mining approach that determines the impact of the emotional valence (textual data quantification) shown by the students in their feedback provided during the semester course (pre and post) to determine its (emotional classification) relation or interconnectedness with the students' learning outcome and performance. The proposed method is designed based on the appraisal theories and component process model (CPM) that studies the degree of pleasantness or goal achievement as an effect of valence judgements. Technically, the study identifies the top terms in the students' data that can be used to draw insights or understand the learning experiences or performance by using Corpus feature selection and Term document matrix word processing libraries in R programming software. Also, it utilized the emotional valence score (quantified data) extracted from the analyzed (textual) data to statistically investigate the correlation and effect of the emotions scores (polarization or intensity of words) expressed by the students with their final grade or learning outcome using Sentiment Analysis package and Statistical analysis methods such as the Spearman's rho (ρ), Kendall's tau (r), and Kruskal-Wallis H-Test in R. The results shows that while in overall the emotional valence of the students do not influence or determine the outcome of their study or final average grade (p>0.05). When considering the analyzed comments: Pre-and Post-Course;it found that emotional scores (valence) expressed by the students in the Post-Course are more closely related or linke

关键词： Contrastive Learning

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Noise2Seg: Automatic Few-Shot Selection from Noisy Web Data for Underwater Tropical Fishes Segmentation

Noise2Seg: Automatic Few-Shot Selection from Noisy Web Data ...

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2023 International Symposium on Networks, computers and Communications, ISNCC 2023

作者： Gilal, Nauman Ullah Majeed, Fahad Al-Thelaya, Khaled Khan, Mehak Schneider, Jens Agus, Marco College of Science and Engineering Hamad Bin Khalifa University Doha Qatar Oslo Metropolitan University AI Lab Department of Computer Science Norway

ISBN: (纸本)9798350335590

We present a novel automatic few-shot selection (FSS) approach with a noisy web-collected dataset for underwater tropical fishes. Underwater image segmentation is of utmost importance in marine biology and underwater robotics. However, obtaining high-quality annotated underwater images is a challenging task due to the scarcity of underwater imaging systems and the high cost of data acquisition. Alternatively, web data is a readily available source of images, but it frequently contains web-corrupted noisy labels, making it challenging to curate a clean dataset for underwater tropical fish segmentation. Generally, the manual selection of high-quality images from web data requires human supervision and expert proofreading, which is both expensive and time-consuming. To address this issue, we propose Noise2Seg, an automatic processing framework composed by: (i) an automated web scrapping tool, (ii) a robust and automatic FSS process using a loss ranking scheme;(iii) a manual annotation component using 'Roboflow', (iv) the latest You Only Look Once version 8 (YOLOv8) for underwater fish detection and segmentation. Additionally, we curated and annotated a novel dataset for the segmentation of tropical fishes from Qatar marine ecosystem (Qatar Tropical Fishes-10 QTF-10). We compared the performance of manual and automatic FSS using mean average precision (mAP) score;manual FSS achieved all mAP (91.5%, 93.4%), and minimum mAP (19.5%, 54.8%), while automatic FSS achieved all mAP (92%, 99.5%), and minimum mAP (24.9%, 99.5%) with 5 and 10 images per class, respectively. The code and dataset related to this paper can be found on GitHub11https://***/GilalNauman/Automatic-Few-shot-Selction-ISNCC/tree/main, accessed on 1st of March 2023. © 2023 IEEE.

关键词： Fish

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Link Prediction Based on Node Centrality Measure 12th

Link Prediction Based on Node Centrality Measure

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12th International Conference on Frontiers of Intelligent Computing: Theory and Applications, FICTA 2024

作者： Nandini, Y.V. Jaya Lakshmi, T. Enduri, Murali Krishna Algorithms and Complexity Theory Lab Department of Computer Science and Engineering SRM University-AP Andhra Pradesh Amaravati India Department of Computing Sheffield Hallam University Sheffield United Kingdom

ISBN: (纸本)9789819601424

Predicting links is crucial task for determining future links in complex networks across different real-world domains like information networks, social interactions, and technological networks. The link prediction method utilizes graph topological features to locate common neighborhood, yet it overlooks the importance of nodes within the network. In this context, we seek to utilize the importance of node in the network in the link prediction techniques. Centrality metrics measure a node’s relative importance within the network and demonstrates a strong correlation with future links in complex networks. In our study, we propose a novel link prediction measure called Local-Similarity based on Summation of Degree Centrality (CLP). CLP finds similarity scores for node pairs by considering common neighbors and use the centrality scores of these common neighbors in the prediction task. To assess our approach, we compare it with existing methods like Jaccard coefficient, Preferential Attachment, and a recent measure like Keyword Network Link Prediction based on degree centrality. We conduct experiments on four real-world datasets, and CLP shows significant improvements. On average, there’s a 15% improvement in Area Under the Receiver Operating Characteristic (AUROC) compared to existing methods and a 27% improvement over the recent one. Additionally, there’s an average 20 and 23% enhancement in Area Under Precision Recall (AUPR) compared to existing and recent methods. Our experiments highlight the superior performance of the proposed CLP method. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Prediction models

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Themis: Adaptive CPU Resource Allocation for VMs on Heterogeneous Multicore Architectures 10

Themis: Adaptive CPU Resource Allocation for VMs on Heteroge...

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10th IEEE Smart World Congress, SWC 2024

作者： Yang, Yecheng Pang, Pu Wang, Jiawen Chen, Quan Chen, Weiwei Li, Li Guo, Minyi Shanghai Jiao Tong University Department of Computer Science and Engineering China Shuhai Lab Huawei Cloud Computing Technologies Co. Ltd China

ISBN: (纸本)9798331520861

Tenants usually rent virtual machines (VMs) to deploy their applications, while those VMs are generally colocated on the same physical machine by the cloud provider to improve resource utilization. Meanwhile, modern CPUs are evolving towards heterogeneous multicore architecture, which integrates powerful brawny cores and power-efficient wimpy cores into a CPU of a machine. This paper focuses on how to allocate CPU resources when co-locating VMs on the heterogeneous architecture. When a VM is created by the Kernel-based Virtual Machine (KVM) virtualization technology, it consists of vCPU threads and Emulator threads. Our investigation shows that properly setting the range of CPU cores where Emulator threads are allowed to run can further improve the VM performance, when using Linux Completely Fair Scheduler (CFS) to schedule VMs. However, it is challenging to find the proper range setting because running different applications in the VM leads to nonidentical optimal range settings, and the VM is usually a black-box from the perspective of the cloud provider. We therefore propose Themis, a runtime system comprising a core scheduler and a load monitor. The core scheduler finds the proper range setting based on low-level information, while the load monitor identifies whether the load of the VM is significantly changed and the range is required to be adjusted. The experimental results show that Themis improves the performance of popular cloud applications by up to 48.4% compared with prior work. © 2024 IEEE.

关键词： Resource allocation

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Towards Secure and Private Smart Contracts in Ethereum: SafeSC ChatGPT-based Tool in Action

Towards Secure and Private Smart Contracts in Ethereum: Safe...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Elghazaly, Osama Nasser, Nidal El Ouadrhiri, Ahmed Ali, Asmaa Alfaisal University IoT Research Lab Riyadh Saudi Arabia Rockford University Department of Mathematics Computer Science and Physics RockfordIL United States Wastern University Electrical and Computer Engineering Department London Canada

ISBN: (纸本)9798350351255

Blockchain-based smart contracts, while transformative, pose privacy concerns due to Ethereum's transparency. To address this, we present Safe Smart Contracts (SafeSC), leveraging zk-SNARKs for privacy without compromising Ethereum's transparency. SafeSC's Python tool facilitates contract understanding and verification without accessing the source code. Our paper explores privacy preservation techniques, favoring Zero-Knowledge Proofs (ZKPs). SafeSC employs zk-SNARKs and Groth-16, achieving a delicate balance between transparency and privacy in smart contract development. The tool's design, covering architecture, assumptions, data flow, and zero-knowledge proof workflow, marks a step toward secure smart contract solutions. We advocate for continued exploration and refinement to enhance blockchain technologies. © 2024 IEEE.

关键词： Blockchain ChatGPT Ethereum Smart Contracts

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IKM_lab at BioLaySumm Task 1: Longformer-based Prompt Tuning for Biomedical Lay Summary Generation 22

IKM_Lab at BioLaySumm Task 1: Longformer-based Prompt Tuning...

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22nd Workshop on Biomedical Natural Language Processing and BioNLP Shared Tasks, BioNLP 2023

作者： Wu, Yu-Hsuan Lin, Ying-Jia Kao, Hung-Yu Intelligent Knowledge Management Lab Department of Computer Science and Information Engineering National Cheng Kung University Tainan Taiwan

ISBN: (纸本)9781959429852

This paper describes the entry by the Intelligent Knowledge Management (IKM) laboratory in the BioLaySumm 2023 task1. We aim to transform lengthy biomedical articles into concise, reader-friendly summaries that can be easily comprehended by the general public. We utilized a long-text abstractive summarization longformer model and experimented with several prompt methods for this task. Our entry placed 10th overall, but we were particularly proud to achieve a 3rd place score in the readability evaluation metric. Our code is available at https://***/IKMlab/ BioLaySumm. © 2023 Association for Computational Linguistics.

关键词： Knowledge management

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